CM4010 Machine Learning Syllabus:

CM4010 Machine Learning Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

This course will make students
1. To learn the basic aspects of machine learning.
2. To get basic knowledge on supervised learning.
3. To realize the importance of unsupervised learning.
4. To exposed on direct and indirect neuro control schemes.
5. To get insight into the basic knowledge on fuzzy logic systems

UNIT-I INTRODUCTION TO MACHINE LEARNING

Course objectives of machine learning – Human learning/ Machine learning – Types of Machine learning:- Supervised Learning – Unsupervised learning – Reinforcement Learning – Evolutionary Learning – Regression – Classification – The Machine Learning Process:- Data Collection and Preparation – Feature Selection – Algorithm Choice – Parameter and Model Selection – Training – Evaluation

UNIT-II SUPERVISED LEARNING

Linearly separable and nonlinearly separable populations – Introduction to ANN: Biological neuron, artificial neuron, activation function, Perceptron, Multi Layer Perceptron–Backpropagation Learning Algorithm – Radial Basis Function Network – Support Vector Machines: – Kernels – Risk and Loss Functions – Support Vector Machine Algorithm –Multi Class Classification – Support Vector Regression

UNIT-III UNSUPERVISED LEARNING

Introduction – Clustering:-Partitioning Methods:- K-means algorithm – Hierarchical clustering –Fuzzy Clustering – Clustering High-Dimensional Data:- Problems – Challenges – Subspace Clustering – Biclustering- Self Organizing Map (SOM) – SOM algorithm

UNIT-IV NEURAL NETWORKS FOR MODELING AND CONTROL

Need for using ANN in Modeling and Control – Modeling of non-linear systems using ANN: Generation of training data, Identification of Optimal architecture, Model validation – Control of nonlinear systems using ANN: Direct and Indirect neuro control schemes – Adaptive neuro controller

UNIT-V FUZZY LOGIC SYSTEMS

Fuzzy set theory – Operation on fuzzy sets: Scalar cardinality, Fuzzy cardinality, Fuzzy union and intersection, Fuzzy complement (Yager and Sugeno), Aggregation, Projection, Composition, Cylindrical extension, Fuzzy relation – Fuzzy membership functions – Modeling of non-linear systems using fuzzy models: Fuzzification, Knowledge base, Decision making logic, Defuzzification.

COURSE OUTCOMES:

Upon completion of this course, students will be able
CO1: To get familiarize with the basic aspects of machine learning.
CO2: To get exposure on supervised and unsupervised learning.
CO3: To demonstrate the need for neutral networks for modelling and control
CO4: To get familiarize with the fuzzy logic systems.
CO5: To realize the importance of machine learning and its applications.

REFERENCES:

1. EthemAlpydin, “Introduction to Machine learning (Adaptive Computation and Machine Learning series)”, MIT Press, 2004.
2. Ferdinand van der Heijden, Robert Duin, Dick de Ridder, David M. J. Tax, Classification, Parameter Estimation, and State Estimation: An Engineering Approach Using MATLAB, John Wiley & Sons, 2005.
3. Klir GJ, and Bo, Yuan, “Fuzzy sets and fuzzy logic, Theory and applications”, Prentice Hall, 1995.
4. Millon WT, Sutton RS and Webrose PJ, “Neural Networks for Control”, MIT press, 1992.